Identity preserved plant molecular farming offers value-added opportunity for farmers.

Bulk commodity row crop production in the United States is frequently subject to narrow profit margins, often complicated by weather, supply chains, trade, and other factors. Farmers seeking to increase profits and hedge against market volatility often seek to diversify their operations, including producing more lucrative or productive crop varieties. Recombinant plants producing animal or other non-native proteins (commonly referred to as plant molecular farming) present a value-added opportunity for row crop farmers. However, these crops must be produced under robust identity preserved systems to prevent comingling with bulk commodities to maintain the value for farmers, mitigate against market disruptions, and minimize any potential food, feed, or environmental risks.

Analysis of banana plant health using machine learning techniques.

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

Exploring the Global Trends of Bacillus, Trichoderma and Entomopathogenic Fungi for Pathogen and Pest Control in Chili Cultivation.

Chili, renowned globally and deeply ingrained in various cultures. Regrettably, the onset of diseases instigated by pests and pathogens has inflicted substantial losses on chili crops, with some farms experiencing complete production decimation. Challenges confronting chili cultivation include threats from pathogenic microbes like Xanthomonas, Fusarium, Phytophthora, Verticillium, Rhizoctonia, Colletotrichium and Viruses, alongside pests such as whiteflies, mites, thrips, aphids, and fruit flies. While conventional farming practices often resort to chemical pesticides to combat these challenges, their utilization poses substantial risks to both human health and the environment. In response to this pressing issue, this review aims to evaluate the potential of microbe-based biological control as eco-friendly alternatives to chemical pesticides for chili cultivation. Biocontrol agents such as Bacillus spp., Trichoderma spp., and entomopathogenic fungi present safer and more environmentally sustainable alternatives to chemical pesticides. However, despite the recognised potential of biocontrol agents, research on their efficacy in controlling the array of pests and pathogens affecting chili farming remains limited. This review addresses this gap by evaluating the efficiency of biocontrol agents, drawing insights from existing studies conducted in other crop systems, regarding pest and pathogen management. Notably, an analysis of Scopus publications revealed fewer than 30 publications in 2023 focused on these three microbial agents. Intriguingly, India, as the world's largest chili producer, leads in the number of publications concerning Bacillus spp., Trichoderma spp., and entomopathogenic fungi in chili cultivation. Further research on microbial agents is imperative to mitigate infections and reduce reliance on chemical pesticides for sustainable chili production.

Cultivation of Limnospira maxima under extreme environmental conditions in Saudi Arabia: Salinity adaptation and scaling-up from laboratory culture to large-scale production.

Limnospira maxima has been adapted to grow in high salinity and in an economically alternative medium using industrial-grade fertilizers under harsh environmental conditions in Saudi Arabia. A sequence of scaling-up processes, from the laboratory to large-scale open raceways, was conducted along with gradual adaptation to environmental stress (salinity, light, temperature, pH). High biomass concentration at harvest point and areal productivity were achieved during the harsh summer season (1.122 g L-1 and 60.35 g m-2 day-1, respectively). The average protein content was found to be above 40 % of dry weight. Changes in the color and morphological appearance of the L. maxima culture were observed after direct exposure to sunlight in the outdoor raceways. These results demonstrate a successful and robust adaptation method for algal cultivation at outdoor large-scale in harsh environment (desert conditions) and also prove the feasibility of using hypersaline seawater (42 g kg-1) as an algal growth medium.

Effect of red and blue light versus white light on fruit biomass radiation-use efficiency in dwarf tomatoes.

The effect of the ratio of red and blue light on fruit biomass radiation-use efficiency (FBRUE) in dwarf tomatoes has not been well studied. Additionally, whether white light offers a greater advantage in improving radiation-use efficiency (RUE) and FBRUE over red and blue light under LED light remains unknown. In this study, two dwarf tomato cultivars ('Micro-Tom' and 'Rejina') were cultivated in three red-blue light treatments (monochromatic red light, red/blue light ratio = 9, and red/blue light ratio = 3) and a white light treatment at the same photosynthetic photon flux density of 300 µmol m-2 s-1. The results evidently demonstrated that the red and blue light had an effect on FBRUE by affecting RUE rather than the fraction of dry mass partitioned into fruits (Ffruits). The monochromatic red light increased specific leaf area, reflectance, and transmittance of leaves but decreased the absorptance and photosynthetic rate, ultimately resulting in the lowest RUE, which induced the lowest FBRUE among all treatments. A higher proportion of blue light (up to 25%) led to a higher photosynthetic rate, resulting in a higher RUE and FBRUE in the three red-blue light treatments. Compared with red and blue light, white light increased RUE by 0.09-0.38 g mol-1 and FBRUE by 0.14-0.25 g mol-1. Moreover, white light improved the Ffruits in 'Rejina' and Brix of fruits in 'Micro-Tom' and both effects were cultivar-specific. In conclusion, white light may have greater potential than mixed red and blue light for enhancing the dwarf tomato FBRUE during their reproductive growth stage.

Small-scale market gardeners' knowledge, attitudes and practices regarding the use of chemical pesticides in the Kabare territory (South-Kivu) in Eastern D.R. Congo.

Damage caused by pests and diseases is one of constraints on crop production for food security. Based on the use of questionnaire and interviews that were conducted in Kabare territory (South-Kivu), this study was carried out to (i) assess farmers practices, attitudes, and knowledge about pesticides use, and (ii) assess the human health and physical environment effects using pesticides. Data was collected from 300 small-scale farmers in study area. Results showed that majority of our respondents were men (59 %) rather than women (41 %) and local knowledge of pesticide use was low (60 %). Education level had a significant influence (p < 0.01) on level of knowledge about pesticide use, time and dose of treatment, method of control, and persistence time. In addition, education level influence significantly farmers' attitudes before and after pesticide treatment (p < 0.05). Pest management control, time of pesticide application, and packaging management method varied significantly with level of local knowledge (p < 0.01). Pesticides use by small-scale farmers has an effect on water, soil, and air quality. It also causes human pathologies such as vomiting, eye irritation, and even loss of life in event of heavy exposure. Inhalation and dermal exposure are main and most dangerous routes of pesticide exposure in our study area, which lacks protective strategies. Finally, use of pesticides disrupts biodiversity through the disappearance of pollinators, predators, parasitoids, and soil microorganisms. Therefore, broad continuity of this study with integration of other scientific aspects would effectively contribute to the improvement of environmental quality.

Effect of cold arid high-altitude environment on bioactive phytochemical compounds of organically grown Brassicaceae vegetables for nutri-health security in mountainous regions.

High-altitude (HA) environment presents immense physiological adversities for humans that have been overcome by supplementing bio-active phytochemicals from functional foods that support and accelerate acclimatization under these extreme environmental conditions. Several agricultural interventions have been investigated to enhance the phytochemical content in vegetables however; these studies have been limited to low-altitude (LA) regions only. In view of an existing knowledge gap, current work is designed to compare the phytochemical compositions of HA and LA-grown Brassicaceae vegetables (cabbage, cauliflower, knol-khol, and radish) using organic treatments via farm yard manure (FYM) and Azotobacter. The open field study was conducted as a two-factorial randomized block design. The first factor was treatment (T1-FYM, T2-Azotobacter, T3-FYM + Azotobacter, and T4-control) while the second was locations (HA and LA). Among all these treatments, the application of treatment T3 in HA-grown cabbage showed the highest total phenolic content (TPC; 9.56 µg/mg), total flavonoids content (TFC; 14.48 µg/mg), and antioxidant potential using 2,2-diphenyl-1-picrylhydrazyl (DPPH; 85.97%) and ferric reducing antioxidant power (FRAP; 30.77 µg/mg) compared to LA grown samples. Reverse Phase high performance liquid chromatography (RP-HPLC) analysis showed that treatment T3 at HA led to significantly high kaempferol (0.92 µg/mg) and sulforaphane (8.94 µg/mg) contents in cabbage whereas, indole-3-carbinol (1.31 µg/mg) was higher in HA grown cauliflower. The present study provides scientific evidence for the enrichment of health-promoting phytochemical compounds in Brassicaceae vegetables grown with T3 treatment specifically at HA.

Evaluating local plant species for effective fall armyworm management strategies in Taiwan.

BACKGROUND: The emergence of Spodoptera frugiperda (fall armyworm; FAW) in the world has raised concerns regarding its impact on crop production, particularly on corn and sorghum. While chemical control and Bt crops have been effective in managing FAW damage, the development of pesticide-resistant and Bt-resistant strains necessitates alternative control methods. The push-pull farming system has gained attention, but direct utilization of African plant species in Taiwan faces challenges due to invasive potential and climatic disparities. Therefore, identifying and evaluating suitable local plant species, such as Napier grass (Pennisetum purpureum), Desmodium species, and signal grass (Brachiaria brizantha), is crucial for implementing effective FAW management strategies in Taiwan. RESULTS: In screening fifty Napier grass germplasms, all demonstrated an antibiotic effect, reducing leaf consumption compared to corn. Notably, thirty-five germplasms exhibited robust antibiotic traits, decreasing FAW consumption and increasing mortality rates. Three Napier grass germplasms also attracted more female moths for oviposition. Further evaluation of selected Napier grass germplasms and signal grass demonstrated efficacy in reducing FAW larval weight and survival duration. Additionally, Desmodium species, particularly D. uncinatum, showed promising toxicity against FAW larvae. CONCLUSION: Our findings support the effectiveness of selected Napier grass germplasms and signal grass as pull plants, and highlight the potential of D. uncinatum as a push plant in FAW management strategies in Taiwan.

Effects of oilseed rape green manure on phosphorus availability of red soil and rice yield in rice-green manure rotation system.

Improving the nutrient content of red soils in southern China is a priority for efficient rice production there. To assess the effectiveness of oilseed rape as green manure for the improvement of soil phosphorus nutrient supply and rice yield in red soil areas, a long-term field plot experiment was conducted comparing two species of rape, Brassica napus (BN) and Brassica juncea (BJ). The effects of returning oilseed rape on soil phosphorus availability, phosphorus absorption, and yield of subsequent rice under rice-green manure rotation mode were analyzed, using data from the seasons of 2020 to 2021. The study found that compared with winter fallow treatment (WT) and no-tillage treatment (NT), the soil available phosphorus content of BN was increased, and that of BJ was significantly increased. The content of water-soluble inorganic phosphorus of BJ increased, and that of BN increased substantially. Compared with the WT, the soil organic matter content and soil total phosphorus content of BN significantly increased, as did the soil available potassium content of BJ, and the soil total phosphorus content of BJ was significantly increased compared with NT. The soil particulate phosphorus content of BJ and BN was significantly increased by 14.00% and 16.00%, respectively. Compared with the WT, the phosphorus activation coefficient of BJ was significantly increased by 11.41%. The rice plant tiller number under the green manure returning treatment was significantly increased by 43.16% compared with the winter fallow treatment. The green manure returning measures increased rice grain yield by promoting rice tiller numbers; BN increased rice grain yield by 9.91% and BJ by 11.68%. Based on these results, returning oilseed rape green manure could augment the phosphorus nutrients of red soil and promote phosphorus availability. Rice-oilseed rape green manure rotation could increase rice grain yield.

Greenhouse gas emissions in the Indian agriculture sector and mitigation by best management practices and smart farming technologies-a review.

The growing demand for agricultural products, driven by the Green Revolution, has led to a significant increase in food production. However, the demand is surpassing production, making food security a major concern, especially under climatic variation. The Indian agriculture sector is highly vulnerable to extreme rainfall, drought, pests, and diseases in the present climate change scenario. Nonetheless, the key agriculture sub-sectors such as livestock, rice cultivation, and biomass burning also significantly contribute to greenhouse gas (GHG) emissions, a driver of global climate change. Agriculture activities alone account for 10-12% of global GHG emissions. India is an agrarian economy and a hub for global food production, which is met by intensive agricultural inputs leading to the deterioration of natural resources. It further contributes to 14% of the country's total GHG emissions. Identifying the drivers and best mitigation strategies in the sector is thus crucial for rigorous GHG mitigation. Therefore, this review aims to identify and expound the key drivers of GHG emissions in Indian agriculture and present the best strategies available in the existing literature. This will help the scientific community, policymakers, and stakeholders to evaluate the current agricultural practices and uphold the best approach available. We also discussed the socio-economic, and environmental implications to understand the impacts that may arise from intensive agriculture. Finally, we examined the current national climate policies, areas for further research, and policy amendments to help bridge the knowledge gap among researchers, policymakers, and the public in the national interest toward GHG reduction goals.

Organic farming practices increase weed density and diversity over conventional practices: A meta-analysis.

Population growth and climate change challenge our food and farming systems and provide arguments for an increased intensification of agriculture. Organic farming has been seen as a promising option due to its eco-friendly approaches during production. However, weeds are regarded as the major hindrance to effective crop production which varies depending on the type of crop and spacing. Their presence leads to reduced yield, increase in harvest cost and lower the qualities of some produce. Thus, weed management is a key priority for successful crop production. Therefore, we conducted a meta-analysis from published studies to quantify possible differences on weed density, diversity and evenness in organic and conventional farming systems and best intervention for weed management in organic farming system. Data included were obtained from 32 studies where 31 studies with 410 observations were obtained for weed density, 15 studies with 168 observations for diversity, and 5 studies with 104 observations for evenness. Standard deviation of mean was obtained from the studies, log transformed using natural logarithms and the effect size pooled using standardized mean difference (SMD). Publication bias was determined through funnel plot. Results showed that organic farming has significant higher weed density (P < 0.01), diversity (P = 0.01), and evenness (P < 0.05) compared to conventional farming. Despite so, diversified crop rotation has been proved to reduce weed density in organic farming by up to 49 % while maize-bean intercropping decrease densities of Amaranthus ssp, Cyperus ssp and Cammelina ssp compared with monocropping. Use of mulch after one hand weeding was found to control up to 98 % of weeds and use of cover crop between 24 % and 85 % depending on the type of the cover crop. The study results show that organic farming encourages high weed density, diversity and evenness but use of the integrated approaches can help to maintain weed density at a manageable level.

Development of a machine vision-based weight prediction system of butterhead lettuce (Lactuca sativa L.) using deep learning models for industrial plant factory.

Introduction: Indoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops non-destructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system.This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory. Methods: A linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction. Results and discussion: The performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R2) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R2 (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops non-destructively and easily. In addition, it would help to decide when to harvest on the spot.

Identifying selection strategies based on the practices and preferences of small ruminant farmers to improve the sustainability of their breeding systems.

Small ruminant farming is of socio-economic and environmental importance to many rural communities around the world. The SMARTER H2020 project aims to redefine genetic selection criteria to increase the sustainability of the sector. The objective of this study was to analyse the selection and breeding management practices of small ruminant producers and breeders, linked with socio-technical elements that shape them. The study is based on farm surveys using semi-structured interviews conducted in five countries (France, Spain, Italy, Greece, and Uruguay) across 272 producers and breeders of 13 sheep and goat breeds, and 15 breed × system combinations. The information was collected in four sections. The first and second sections dealt with general elements of structure and management of the system and the flock/herd. The third section focused on selection and breeding management practices: criteria for culling and replacement of females, selection criteria for males, use of estimated breeding values and global indexes, and preferences for indexing new traits to increase the sustainability of their system. The fourth section aimed to collect socio-technical information. We used a data abstraction method to standardise the representation of these data. A mixed data factor analysis followed by a hierarchical ascending classification allowed the characterisation of three profiles of selection and breeding management: (1) a profile of producers (n = 93) of small flocks/herds, with little knowledge or use of genetic selection and improvement tools (selection index, artificial insemination, performance recording); these farmers do not feel that new traits are needed to improve the sustainability of their system. (2) a profile of producers (n = 34) of multibreed flocks/herds that rely significantly on grazing; they are familiar with genetic tools, they currently use AI; they would like the indexes to include more health and robustness characteristics, to make their animals more resistant and to increase the sustainability of their system. And (3) a profile of producers or breeders (n = 145) of large flocks/herds, with specific culling criteria; these farmers are satisfied with the current indexes to maintain the sustainability of their system. These results are elements that can be used by private breeding companies and associations to support the evolution of selection objectives to increase the resilience of animals and to improve the sustainability of the small ruminant breeding systems.

An Integrated Smart Pond Water Quality Monitoring and Fish Farming Recommendation Aquabot System.

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer's manual labor.

Feed intake in housed dairy cows: validation of a three-dimensional camera-based feed intake measurement system.

Measuring feed intake accurately is crucial to determine feed efficiency and for genetic selection. A system using three-dimensional (3D) cameras and deep learning algorithms can measure the volume of feed intake in dairy cows, but for now, the system has not been validated for feed intake expressed as weight of feed. The aim of this study was to validate the weight of feed intake predicted from the 3D cameras with the actual measured weight. It was hypothesised that diet-specific coefficients are necessary for predicting changes in weight, that the relationship between weight and volume is curvilinear throughout the day, and that manually pushing the feed affects this relationship. Twenty-four lactating Danish Holstein cows were used in a cross-over design with four dietary treatments, 2 × 2 factorial arranged with either grass-clover silage or maize silage as silage factor, and barley or dried beet pulp as concentrate factor. Cows were adapted to the diets for 11 d, and for 3 d to tie-stall housing before camera measurements. Six cameras were used for recording, each mounted over an individual feeding platform equipped with a weight scale. When building the predictive models, four cameras were used for training, and the remaining two for testing the prediction of the models. The most accurate predictions were found for the average feed intake over a period when using the starting density of the feed pile, which resulted in the lowest errors, 6% when expressed as RMSE and 5% expressed as mean absolute error. A model including curvilinear effects of feed volume and the impact of manual feed pushing was used on a dataset including daily time points. When cross-validating, the inclusion of a curvilinear effect and a feed push effect did not improve the accuracy of the model for neither the feed pile nor the feed removed by the cow between consecutive time points. In conclusion, measuring daily feed intake from this 3D camera system in the present experimental setup could be accomplished with an acceptable error (below 8%), but the system should be improved for individual meal intake measurements if these measures were to be implemented.

Exploring individual responses to welfare issues in growing-finishing pig feeding behaviour.

The feeding behaviour of individual growing-finishing pigs can be continuously monitored using sensors such as electronic feeding stations (EFSs), and this could be further used to monitor pig welfare. To make accurate conclusions about individual pig welfare, however, it is important to know whether deviations in feeding behaviour in response to welfare issues are shown only on average or by each individual pig. Therefore, this study aimed (1) to quantify the individual variation in feeding behaviour changes in response to a range of welfare issues, and (2) to explain this individual variation by quantifying the responses to welfare issues for specific subgroups of pigs. We monitored four rounds of 110 growing-finishing pigs each (3-4 months per round). We collected feeding behaviour data using IVOG® EFSs and identified health issues and heat stress using climate sensors and twice-weekly health observations. For each pig, a generalised additive model was fitted, which modelled feeding behaviour through time and estimated the effect of each welfare issue that the pig had suffered from. The range of these effect estimates was compared between pigs to study the individual variation in responses. Subsequently, pigs were repeatedly grouped using physical and feeding characteristics, and, with meta-subset analysis, it was determined for each group whether a deviation in response to the welfare issue (i.e. their combined effect estimates) was present. We found that the range in effect estimates was very large, approaching normal distributions for most combinations of welfare issues and feeding variables. This indicates that most pigs did not show feeding behaviour deviations during the welfare issue, while those that did could show both increases and reductions. One exception was heat stress, for which almost all pigs showed reductions in their feed intake, feeding duration and feeding frequency. When looking at subgroups of pigs, it was seen that especially for lameness and tail damage pigs with certain physical characteristics or feeding strategies did consistently deviate on some feeding components during welfare issues (e.g. only relatively heavier pigs reduced their feeding frequency during lameness). In conclusion, while detection of individual pigs suffering from heat stress using feeding variables should be feasible, detection of (mild) health issues would be difficult due to pigs responding differently, if at all, to a given health issue. For some pigs with specific physical or behavioural characteristics, nevertheless, detection of some health issues, such as lameness or tail damage, may be possible.

Multitrait genetic parameter estimates in a Tenebrio molitor reference population: high potential for breeding gains.

To address multiple issues impacting the climate imbalance, insects, and in particular Tenebrio molitor, represent now a promising alternative for producing high-quality protein products with low environmental impact. As with any new species farmed on an industrial scale, insect breeding production must be improved through the accumulation of knowledge on rearing techniques and genetic management. Little information on the inheritance of agronomically interesting traits, dedicated to Tenebrio molitor, is available. This study aims to decipher the genetic parameters (heritability and genetic correlations) of reproduction, larval growth and survival, pupation rate and developmental time from a reference population made up of 1 931 sib-groups reared under pedigree, in controlled and stable environments and generated with single pair mating. Considering all sib-groups, 29 599 offspring have been generated and phenotyped over four generations to support this study and provide enough data to estimate, under linear animal models, the additive genetic and common environmental effects. Phenotypic analyses underlined an important variability among sib-groups and individuals, as for the total oviposition during 4 weeks counting (0-680 eggs, min - max, respectively) or larval body mass 63 days posteclosion (36.3-206.8 mg, min - max, respectively). Moderate to important heritability values have been obtained and ranged from 0.17 to 0.54 for reproduction phenotypes, 0.10-0.44 for growth parameters, 0.06-0.22 for developmental time and 0.10-0.17 for larval survival rates. The proportion of phenotypic variance explained by the environmental part varyies from 0.10 to 0.36 for reproductive traits, from 0.17 to 0.38 for growth parameters, from 0.06 to 0.36 for developmental time and 0.17-0.22 for survival rates. Genetic correlations underline relationships among phenotypes such as the trade-off between developmental time from egg to pupae and pupae weight (r2 = 0.48 ± 0.06). These important phenotypic variations coupled with promising heritability values pave the road for future breeding programs in Tenebrio molitor.

Light manipulation as a route to enhancement of antioxidant properties in red amaranth and red lettuce.

With the growing global population and climate change, achieving food security is a pressing challenge. Vertical farming has the potential to support local food production and security. As a Total Controlled Environment Agriculture (TCEA) system, vertical farming employs LED lighting which offers opportunities to modulate light spectrum and intensity, and thus can be used to influence plant growth and phytochemical composition, including antioxidants beneficial for human health. In this study, we investigated the effect of four red-to-blue light ratios of LEDs (R:B 1, 2.5, 5 and 9) on the growth and antioxidant components in red amaranth microgreens and red lettuce. Plant growth, total phenols, betalains, anthocyanins, vitamin C and antioxidant capacity (ferric reducing antioxidant power assay) were evaluated. A higher proportion of red light resulted in biometric responses, i.e., stem elongation in red amaranth and longer leaves in red lettuce, while the increase in the blue light fraction led to the upregulation of antioxidative components, especially total phenols, betalains (in red amaranth) and anthocyanins (in red lettuce). The antioxidant capacity of both crops was strongly positively correlated with the levels of these phytochemicals. Optimizing the red-to-blue ratio in LED lighting could be effective in promoting antioxidant-rich crops with potential health benefits for consumers.

The comparative advantage of urban goat production.

BACKGROUND: Urban agriculture significantly contributes to food security. The two primary components of urban agriculture are livestock production and horticulture. The goat, Capra hircus, is one of the commonly raised food animals. Goats can be used to generate income, produce milk, meat, skins, furs (hairs) and manure and provide various sociocultural and ecological services. OBJECTIVES: This study evaluates the significance of urban goat production and recommends ways to lessen the adverse impacts of urban goat production. METHODS: This report involved an in-depth interview with seven key informants in Adama and Addis Ababa cities. RESULTS: Goats can thrive in limited urban open spaces, scavenge leftovers from homes and open markets and browse on open public land. Goats can be incorporated into urban agriculture, in so doing contributing to a circular economy. Goats can thrive on a limited supply of water and feed and require less care and space. Goat farming is used to mitigate the adverse impact of climate change. Goats are naturally active, which makes them better at avoiding traffic accidents. Goats can be used to control bush encroachment. Goat farming in cities improves land use efficiency and food security. Being friendly animals, goats can be utilized to play with kids, and they can be a basic piece of metropolitan ecotourism. However, goats can harm urban green spaces; therefore, to avoid issues of this kind, goat production must be zoned. CONCLUSIONS: Urban goat farming could add a new dimension to urban food security. Extensive pieces of empirical evidence need to be generated to enhance the adoption of urban goat farming.

The Impact of Food Waste Compost, Vermicompost, and Chemical Fertilizers on the Growth Measurement of Red Radish (Raphanus sativus): A Sustainability Perspective in the United Arab Emirates.

The pressing need for sustainable agricultural practices, especially with the increasing population, has directed attention towards alternative fertilizers that enhance crop yield while preserving soil integrity and reducing food loss. The current study investigated the comparative efficacy of food waste compost (FOWC), vermicompost, and chemical fertilizers on the growth of red radish. The present work used a systematic experimental design to evaluate plant growth parameters, including radish weight and height. The soil quality was determined by measuring the pH and electrical conductivity for all soil samples. The results indicated a significant variation in red radish fresh weight among different treatments. For example, the 25% vegetable and fruit waste compost (VFWC) treatment demonstrated a relatively high mean fresh weight, while the 50% mixed compost (MC) treatment yielded a much lower mean fresh weight. These numbers underscore the potential efficacy of specific food waste treatments in enhancing plant growth, with vermicompost at 50% and VFWC at 25% showing considerable promise in increasing crop yield. The current study concluded that FOWC and vermicompost significantly improved plant growth, advocating for their use as sustainable and environmentally friendly alternatives to chemical fertilizers. The current findings emphasized the importance of selecting appropriate fertilizer types and concentrations to optimize agricultural productivity and environmental sustainability, supporting the incorporation of food waste into agricultural systems as a beneficial resource.